You may be interested in this report from earlier this year: https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways

And a breakdown of it that gives a bit of an alternate explanation of their numbers (and more closely matches what we're seeing IMO): https://unessays.substack.com/p/talk-is-cheap

Short version is that what you, I, and other responses to you are seeing is backed up by the numbers: AI code generation leads to significant bottlenecks, burnout, increased bugs, insane code churn (rewrites), and decreased feature deployment (presumably due to dealing with the bugs and bottlenecks). And as of when this was done, increased AI did not improve the situation, it generally made it worse.

The code churn stat is what bothers me the most: People keep portraying the whole "quickly rewrite/re-generate code with AI" thing as a feature, but looking at it over time, it actually means you lose your battle-tested code and no longer have a stable foundation to build on top of. That's where I think the major problems will show up as code needs to be maintained - and, seemingly, already has been.